{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/capsule-neural-networks-for-graph","title":"Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations","arxiv_id":"1902.08399","date":"2019-02-22","proceeding":null,"authors":["Marcelo Daniel Gutierrez Mallea","Peter Meltzer","Peter J. Bentley"],"abstract":"Graph classification is a significant problem in many scientific domains. It\naddresses tasks such as the classification of proteins and chemical compounds\ninto categories according to their functions, or chemical and structural\nproperties. In a supervised setting, this problem can be framed as learning the\nstructure, features and relationships between features within a set of labelled\ngraphs and being able to correctly predict the labels or categories of unseen\ngraphs.\n  A significant difficulty in this task arises when attempting to apply\nestablished classification algorithms due to the requirement for fixed size\nmatrix or tensor representations of the graphs which may vary greatly in their\nnumbers of nodes and edges. Building on prior work combining explicit tensor\nrepresentations with a standard image-based classifier, we propose a model to\nperform graph classification by extracting fixed size tensorial information\nfrom each graph in a given set, and using a Capsule Network to perform\nclassification.\n  The graphs we consider here are undirected and with categorical features on\nthe nodes. Using standard benchmarking chemical and protein datasets, we\ndemonstrate that our graph Capsule Network classification model using an\nexplicit tensorial representation of the graphs is competitive with current\nstate of the art graph kernels and graph neural network models despite only\nlimited hyper-parameter searching.","url_abs":"http://arxiv.org/abs/1902.08399v1","url_pdf":"http://arxiv.org/pdf/1902.08399v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-classification","task_name":"Graph Classification"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"}],"methods":[{"method_slug":"fixcaps","method_name":"Capsule Network"},{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/graph-classification-on-dd","task":"Graph Classification","dataset":"D&D","model":"BC + Capsules","rank_in_archive_order":46,"of":53,"metrics":{"Accuracy":"74.86%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-enzymes","task":"Graph Classification","dataset":"ENZYMES","model":"BC + Capsules","rank_in_archive_order":52,"of":54,"metrics":{"Accuracy":"27%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-mutag","task":"Graph Classification","dataset":"MUTAG","model":"BC + Capsules","rank_in_archive_order":32,"of":74,"metrics":{"Accuracy":"88.9%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-nci1","task":"Graph Classification","dataset":"NCI1","model":"BC + Capsules","rank_in_archive_order":68,"of":69,"metrics":{"Accuracy":"65.9%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-nci109","task":"Graph Classification","dataset":"NCI109","model":"BC + Capsules","rank_in_archive_order":38,"of":38,"metrics":{"Accuracy":"58.04"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-proteins","task":"Graph Classification","dataset":"PROTEINS","model":"BC + Capsules","rank_in_archive_order":83,"of":103,"metrics":{"Accuracy":"74.1%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-ptc","task":"Graph Classification","dataset":"PTC","model":"BC + Capsules","rank_in_archive_order":14,"of":37,"metrics":{"Accuracy":"69%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}